Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.global climate impacts | disease modeling | uncertainty H ealth priorities vary between countries and also change significantly over time. One of the factors that governments are concerned with preparing for over decadal timescales is the potential impact that environmental and climate change may have on health and welfare (1, 2). These impacts are complex and multifaceted and include the potential for changing climate to alter in both time and space the burden of vector-borne diseases, including malaria.Malaria causes a significant burden of disease at the global and regional level (3). Malaria is a mosquito-borne infectious disease caused by parasitic protozoans of the genus Plasmodium (vivax, malariae, ovale, knowlesi, and falciparum) and is transmitted by female mosquito vectors of the Anopheles species. The spatial limits of the distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. In endemic areas where transmission occurs in regular long seasons, fatality rates are highest among children who have not yet developed immunity to the disease. In epidemic areas where malaria transmission occurs in short seasons or sporadically in the form of epidemics it is likely to cause severe fatalities in all age categories. Following the Global Malaria eradication program launched by the ...
Global climate change will increase outdoor and indoor heat loads, and may impair health and productivity for millions of working people. This study applies physiological evidence about effects of heat, climate guidelines for safe work environments, climate modeling, and global distributions of working populations to estimate the impact of 2 climate scenarios on future labor productivity. In most regions, climate change will decrease labor productivity, under the simple assumption of no specific adaptation. By the 2080s, the greatest absolute losses of population-based labor work capacity (in the range 11% to 27%) are seen under the A2 scenario in Southeast Asia, Andean and Central America, and the Caribbean. Increased occupational heat exposure due to climate change may significantly impact on labor productivity and costs unless preventive measures are implemented. Workers may need to work longer hours, or more workers may be required, to achieve the same output and there will be economic costs of lost production and/or occupational health interventions against heat exposures.
Background: Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health.Objectives: We developed a model for estimating future undernutrition that accounts for food and nonfood (socioeconomic) causes and can be linked to available regional scenario data. We estimated child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa (SSA) in 2050.Methods: We used current national food availability and undernutrition data to parameterize and validate a global model, using a process-driven approach based on estimations of the physiological relationship between a lack of food and stunting. We estimated stunting in 2050 using published modeled national calorie availability under two climate scenarios and a reference scenario (no climate change).Results: We estimated that climate change will lead to a relative increase in moderate stunting of 1–29% in 2050 compared with a future without climate change. Climate change will have a greater impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62% (South Asia).Conclusions: Climate change is likely to impair future efforts to reduce child malnutrition in South Asia and SSA, even when economic growth is taken into account. Our model suggests that to reduce and prevent future undernutrition, it is necessary to both increase food access and improve socioeconomic conditions, as well as reduce greenhouse gas emissions.
Diarrhoea rates are influenced by weather and climate; transmission can be affected by temperature and rainfall extremes, although few studies have quantified this effect. We undertook a global cross-sectional study of diarrhoea incidence in children under 5, drawing on studies published in the last 50 yr, and assessed the association with climate variables. Log-linear regression was used to quantify any association, controlling for the effects of age, socio-economic conditions and access to improved water and sanitation. We found a negative association between rainfall and diarrhoea rates, with a 4% increase in diarrhoea incidence (95% confidence interval, CI: 1-7%, p = 0.02) for each 10 mm mo -1 decrease in rainfall. Little evidence for association with temperature or climate type was found. Our result for rainfall is consistent with a similar study covering a smaller geographic region. Though biases cannot be excluded, the most likely mechanism is that low rainfall leads to water scarcity, which in turn leads to the use of unprotected water sources and reduces hygiene practices. In the future, greater numbers are expected to experience water scarcity, which may lead to more diarrhoea cases in some locations. This study lends support to programmes for hygiene and water and sanitation coverage, as well as lending support to actions to adapt to and mitigate climate change.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.